modern, enterprise-ready business intelligence web application
- Free • Open Source
- Business Intelligence Tool
What is Apache Superset?
Apache Superset is a data exploration and visualization web application.
- An intuitive interface to explore and visualize datasets, and create interactive dashboards.
- A wide array of beautiful visualizations to showcase your data.
- Easy, code-free, user flows to drill down and slice and dice the data underlying exposed dashboards. The dashboards and charts acts as a starting point for deeper analysis.
- A state of the art SQL editor/IDE exposing a rich metadata browser, and an easy workflow to create visualizations out of any result set.
- An extensible, high granularity security model allowing intricate rules on who can access which product features and datasets. Integration with major authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, ...)
- A lightweight semantic layer, allowing to control how data sources are exposed to the user by defining dimensions and metrics
- Out of the box support for most SQL-speaking databases
- Deep integration with Druid allows for Superset to stay blazing fast while slicing and dicing large, realtime datasets
- Fast loading dashboards with configurable caching
Apache Superset Screenshots
Apache Superset Features
Apache Superset information
- 52,147 Stars
- 10,658 Forks
- 1352 Open Issues
Comments and Reviews
- Data Analysis
- Web Analytics
- Business Intelligence
CategoryNetwork & Admin
Recent user activities on Apache Superset
- atomicWeb added Apache Superset as alternative(s) to RootDB
- YanzuFounder liked Apache SupersetYa
- Jpy added Apache Superset as alternative(s) to Datami
The big problem with this very beautiful tool is that every chart can be represented in the true end by only a single query, so you CANNOT take data from more than one single dataset for chart, until you write off a custom virtual dataset with the data you need.
But the critical example where you have a single chart with two lines, one from a 600k lines table with a time-serie and a second one from a 200M with a different time-serie, you need to """merge""" them together as a single dataset, with a lot of heavy lifting with absolutely no reason to be.